Abstract:
Alzheimer’s Disease (AD) poses a significant global health challenge, underscoring the
need for advanced tools facilitating early and precise diagnosis. Previous studies have em-
ployed various traditional machine learning techniques, transitioning from image decom-
position methods such as principal component analysis to more sophisticated non-linear
decomposition algorithms. The advent of computer vision and neural networks has further
propelled advancements in the biomedical field. Given the absence of a definitive cure for
Alzheimer’s in the medical industry and the significance of formulating effective treatment
strategies through early diagnosis, the relevance of early detection is paramount. This study
centers on Alzheimer’s disease diagnosis through MRI data analysis, leveraging radiomic
features. It introduces a novel 3D deep learning model integrated with attention modules to
tackle the vanishing gradient problem. Additionally, it outlines the development of a machine
learning (ML) model for classification using radiomic features extracted from NIFTI files.
The project introduces Alz3Dnet, a network that integrates attention modules and addresses
vanishing gradient challenges, achieving a validation accuracy of 92.22%. Furthermore, an
ML ensemble model utilizing radiomic features attains an accuracy of 96%
